CN115860396A - Intelligent planning method based on land reserve analysis model - Google Patents
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Abstract
The invention discloses an intelligent planning method based on a land reserve analysis model, which comprises the following steps: s1, collecting a characteristic data item and an analysis model body for constructing a land reserve analysis model, and constructing an evaluation model of the land reserve analysis model by using the characteristic data item and the analysis model body for constructing the land reserve analysis model; and S2, planning a land reserve analysis model with the highest adaptation degree with the target land at the target land as a target land reserve analysis model by using the evaluation model based on the characteristic data item at the target land. The method realizes the customized construction of the land reserve analysis model with the highest adaptation degree for the target land, and improves the accuracy of the land reserve analysis result of the target land.
Description
Technical Field
The invention relates to the technical field of land reserve analysis, in particular to an intelligent planning method based on a land reserve analysis model.
Background
The land reserve analysis is generally to analyze the land reserve situation by utilizing the characteristic data of the target land and perform a series of city planning according to the land reserve analysis result, so that it is very important to construct a proper land reserve analysis model for the target land and obtain an accurate analysis result.
The prior art CN202011630620.5 discloses an intelligent site selection system based on a land reserve implementation monitoring model, which comprises a land reserve supply item library: providing construction land data in a set area, and adjusting and updating in real time according to legal planning; the land reserve implementation monitoring module comprises: the method comprises the steps of taking the constructable land as a basic unit, integrating planning, current situation and approval data in real time, interpreting land reserve implementation stages and identifying potential land; the item site selection module: potential land data are called, two land schemes are automatically generated based on a genetic addressing algorithm and a GIS addressing algorithm, and an optimal addressing scheme is solved by utilizing a set-pair analysis method for comprehensive comparison and selection. The method can intelligently identify the candidate land blocks with development potential by dynamically tracking the land approval state, change the problems of subjectivity and difficult land falling of the traditional site selection mode, carry out global optimization search by using an intelligent algorithm, find the optimum land layout under the constraint of site selection targets, and ensure the maximization of the comprehensive benefit of project site selection.
The prior art can analyze the land reserve to a certain degree, but is difficult to acquire an analysis model most suitable for the target land, so that the accuracy of land reserve analysis of a land area is reduced.
Disclosure of Invention
The invention aims to provide an intelligent planning method based on a land reserve analysis model, and aims to solve the technical problems that in the prior art, an analysis model most suitable for target land is difficult to obtain, and the accuracy of land reserve analysis of a land area is further reduced.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
an intelligent planning method based on a land reserve analysis model comprises the following steps:
s1, collecting a characteristic data item and an analysis model body for constructing a land reserve analysis model, and constructing an evaluation model of the land reserve analysis model by using the characteristic data item and the analysis model body for constructing the land reserve analysis model;
s2, planning a land reserve analysis model with the highest adaptation degree with the target land at the target land as a target land reserve analysis model by utilizing the evaluation model based on the characteristic data item at the target land;
and S3, performing land reserve analysis on the target land by using the target land reserve analysis model.
As a preferable aspect of the present invention, the collecting feature data items and analysis model bodies for constructing a land reserve analysis model includes:
collecting a plurality of land areas subjected to land reserve analysis as a plurality of land samples, and extracting characteristic data items of the land samples used for land reserve analysis in each analysis time period and analysis results of the land reserve in each analysis time period;
a plurality of learning models for data analysis in the machine learning model are collected as a plurality of analysis model bodies.
As a preferred aspect of the present invention, the constructing of the evaluation model of the land reserve analysis model using the characteristic data item and the analysis model body for constructing the land reserve analysis model includes:
randomly splitting the characteristic data items of each land sample in each analysis time interval and the analysis results of each analysis time interval to obtain a training set and a test set of each land sample, selecting the training set of each land sample to be applied to each analysis model body one by one to carry out deep learning to obtain a plurality of land reserve analysis models of each land sample, and calculating the accuracy of each land reserve analysis sample model of each land sample based on the test set;
taking a characteristic data item corresponding to a land reserve analysis model and an analysis model body corresponding to the land reserve analysis model as input items of a BP (back propagation) neural network, taking a correct rate corresponding to the land reserve analysis model as output items of the BP neural network, and carrying out convolution training by using the BP neural network based on the input items of the BP neural network and the output items of the BP neural network to obtain the evaluation model, wherein the model expression of the evaluation model is as follows:
P＝BP(Data,Model)；
in the formula, P is the accuracy, data is the characteristic Data item, model is the analysis Model body, and BP is the BP neural network.
As a preferred scheme of the present invention, the randomly splitting the feature data items of each land sample in each analysis time interval and the analysis results of each analysis time interval to obtain a training set and a test set of each land sample at corresponding time intervals includes:
selecting a characteristic data item of the land sample in the ith analysis period and an analysis result of the ith analysis period to form a training set, wherein a function expression of the training set is as follows: [ Data i ,Result i ] m ；
Selecting a characteristic data item of a land sample in a jth analysis time interval and an analysis result of the jth analysis time interval to form a training set, wherein a function expression of the training set is as follows: [ Data j ,Result j ] n ；
Wherein Data i And Data j Respectively, the characteristic data item of the ith analysis period and the characteristic data item, result, of the jth analysis period i And Result j The analysis result of the ith analysis time interval and the analysis result of the jth analysis time interval are represented by i ≠ j, m is n =4 and is the number of the analysis time intervals in the training set, n is the number of the analysis time intervals in the testing set, and i and j are metering constants;
setting a randomly selected constraint condition as that the data dispersion difference of a training set and a test set is within a threshold range, wherein a calculation formula of the data dispersion difference of the training set and the test set is as follows:
in the formula, H is the data dispersion difference,
for the data spread of the training set, <' > H>
Is the dispersion of data in the test set.
As a preferred aspect of the present invention, the constructing of the target land reserve analysis model includes:
acquiring a characteristic data item at a target soil location, combining the characteristic data item with a plurality of analysis model bodies, bringing the combined characteristic data item into an evaluation model in sequence, and outputting the accuracy of the characteristic data item and each analysis model body by the evaluation model;
selecting the analysis model body corresponding to the highest accuracy as the analysis model body of the target land, carrying out deep learning on the characteristic data item of the target land and the analysis model body of the target land to obtain a land reserve analysis model of the target land, and taking the land reserve analysis model of the target land as the target land reserve analysis model.
As a preferable scheme of the present invention, the analyzing the land reserve of the target land by using the target land reserve analysis model comprises:
and monitoring the characteristic data item of the target land in real time to obtain a real-time characteristic data item, and inputting the real-time characteristic data item into the target land reserve analysis model to obtain a real-time analysis result of the target land reserve.
As a preferred aspect of the present invention, the feature data item includes at least one feature component data, and the function of the feature data item is expressed as: data = [ ]] k ；
The analysis result comprises at least one result component data, and the function expression of the analysis result is as follows: result = [ Result =] r K is not less than 1, r is not less than 1, k and r are measurement constants.
As a preferable aspect of the present invention, the feature data item is formatted to a data format that can be processed by each analysis model body before deep learning by using the analysis model body.
As a preferable aspect of the present invention, when the number of the analysis model bodies corresponding to the highest accuracy is not unique, the analysis model body corresponding to the highest calculation speed is selected from the plurality of analysis model bodies corresponding to the highest accuracy as the analysis model body of the target land.
As a preferable aspect of the present invention, each feature component data in the feature data item is normalized.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of constructing an evaluation model of a land reserve analysis model by utilizing a characteristic data item and an analysis model body for constructing the land reserve analysis model, planning a land reserve analysis model with the highest adaptation degree with a target land at the target land as the target land reserve analysis model by utilizing the evaluation model based on the characteristic data item at the target land, realizing customized construction of the land reserve analysis model with the highest adaptation degree for the target land, and improving the accuracy of a land reserve analysis result of the target land.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of an intelligent planning method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the invention provides an intelligent planning method based on a land reserve analysis model, which comprises the following steps:
s1, collecting a characteristic data item and an analysis model body for constructing a land reserve analysis model, and constructing an evaluation model of the land reserve analysis model by using the characteristic data item and the analysis model body for constructing the land reserve analysis model;
collecting feature data items and an analysis model body for constructing a land reserve analysis model, comprising:
collecting a plurality of land areas subjected to land reserve analysis as a plurality of land samples, and extracting characteristic data items of the land samples used for land reserve analysis in each analysis time period and analysis results of the land reserve in each analysis time period;
a plurality of learning models for data analysis in the machine learning model are collected as a plurality of analysis model bodies.
The feature data items include, but are not limited to, a land geological environment, a land area, a land population distribution, a land industry distribution, a land traffic situation, and the analysis model body includes, but is not limited to, a neural network, a classifier, wherein if it is required to analyze whether the target land can be used as a certain type of building plan, such as building an airport, building a park facility, etc., the classifier can be used to predict a facility category suitable for building the target land based on the land geological environment, the land area, the land population distribution, the land industry distribution, the land traffic situation, or the neural network model can be used to predict a park building area, a scale size, etc. according to the land geological environment, the land area, the land population distribution, the land industry distribution, the land traffic situation.
The method for constructing the evaluation model of the land reserve analysis model by utilizing the characteristic data item and the analysis model body for constructing the land reserve analysis model comprises the following steps of:
randomly splitting the characteristic data items of each land sample in each analysis time interval and the analysis results of each analysis time interval to obtain a training set and a test set of each land sample, selecting the training set of each land sample to be applied to each analysis model body one by one to carry out deep learning to obtain a plurality of land reserve analysis models of each land sample, and calculating the accuracy of each land reserve analysis sample model of each land sample based on the test set;
taking a characteristic data item corresponding to the land reserve analysis model and an analysis model body corresponding to the land reserve analysis model as input items of a BP (back propagation) neural network, taking a correct rate corresponding to the land reserve analysis model as output items of the BP neural network, and carrying out convolution training by using the input items of the BP neural network and the output items of the BP neural network to obtain an evaluation model, wherein the model expression of the evaluation model is as follows:
P＝BP(Data,Model)；
in the formula, P is the accuracy, data is the characteristic Data item, model is the analysis Model body, and BP is the BP neural network.
Randomly splitting the characteristic data items of each land sample in each analysis time interval and the analysis results of each analysis time interval to obtain a training set and a test set of each land sample, wherein the training set and the test set comprise:
selecting a characteristic data item of the land sample in the ith analysis period and an analysis result of the ith analysis period to form a training set, wherein a function expression of the training set is as follows: [ Data ] i ,Result i ] m ；
Selecting a characteristic data item of the land sample in the jth analysis time interval and an analysis result of the jth analysis time interval to form a training set, wherein a function expression of the training set is as follows: [ Data j ,Result j ] n ；
Wherein the Data i And Data j Respectively the characteristic data item of the ith analysis period and the characteristic data item, result, of the jth analysis period i And Result j The analysis result of the ith analysis time interval and the analysis result of the jth analysis time interval are represented by i ≠ j, m is n =4 and is the number of the analysis time intervals in the training set, n is the number of the analysis time intervals in the testing set, and i and j are metering constants;
setting the randomly selected constraint condition as that the data dispersion difference of the training set and the test set is within a threshold range, wherein the calculation formula of the data dispersion difference of the training set and the test set is as follows:
in the formula, H is the data dispersion difference,
for the data spread of the training set, <' > H>
Is the dispersion of data in the test set. />
The data dispersion difference is similar, so that the distribution dispersion of the data in the training set and the test set is similar, the uniformity of the data is realized, and the phenomenon of under-fitting or over-fitting of the training model is avoided.
The conditions of land areas are different, some land areas are developed partially and have partial industry distribution, the characteristic data items of the land areas have characteristic data components describing the industry distribution, some land areas are not developed at all and have no industry distribution, and the characteristic data items of the land areas do not have the characteristic data components describing the industry distribution, so the characteristic data items of the land areas are different, analysis model bodies for deeply learning the characteristic data items also have different result acquisition rates according to the difference of the characteristic data items, the embodiment matches the characteristic data items with the analysis model bodies, calculates the accuracy rate of analysis results generated by combining the characteristic data items with the analysis model bodies in each land sample, carries out model construction through the characteristic data items of the land samples, the analysis model bodies and the accuracy rate to obtain an evaluation model, and the evaluation model embodies the mapping relation between the characteristic data items, the analysis model bodies and the accuracy rate.
And then, according to the characteristic data item of the target land and the analysis model body, the accuracy of a land reserve analysis model constructed by the characteristic data item and the analysis model body can be measured. The individual adaptation can be realized, namely, the land reserve analysis model with the highest accuracy is selected from the plurality of land reserve analysis models for the target land, the land reserve analysis model is selected without comparing the analysis results after the analysis of each land reserve analysis model is repeatedly performed, the land reserve analysis model with the highest accuracy is determined firstly and then analyzed, only the analysis of one model is needed, the analysis steps are reduced, and the analysis efficiency is improved.
S2, planning a land reserve analysis model with the highest adaptation degree with the target land at the target land as a target land reserve analysis model by utilizing the evaluation model based on the characteristic data item at the target land;
constructing a target land reserve analysis model, comprising:
acquiring a characteristic data item at a target soil location, combining the characteristic data item with a plurality of analysis model bodies, bringing the combined characteristic data item into an evaluation model in sequence, and outputting the accuracy of the characteristic data item and each analysis model body by the evaluation model;
and selecting the analysis model body corresponding to the highest accuracy as the analysis model body of the target land, performing deep learning on the characteristic data item of the target land and the analysis model body of the target land to obtain a land reserve analysis model of the target land, and taking the land reserve analysis model of the target land as the target land reserve analysis model.
And when the number of the analysis model bodies corresponding to the highest accuracy is not unique, selecting the analysis model body corresponding to the highest operation speed from the plurality of analysis model bodies corresponding to the highest accuracy as the analysis model body of the target land.
The target land reserve analysis model has the highest adaptability to the target land, and can ensure that the land reserve analysis of the target land has the result closest to the real condition, namely the accuracy of the analysis result is highest.
And S3, performing land reserve analysis on the target land by using the target land reserve analysis model.
Performing land reserve analysis on the target land by using the target land reserve analysis model, wherein the land reserve analysis comprises the following steps:
and monitoring the characteristic data item of the target land in real time to obtain a real-time characteristic data item, and inputting the real-time characteristic data item into the target land reserve analysis model to obtain a real-time analysis result of the target land reserve.
The characteristic data item includes at leastA feature component data, the function of the feature data term being expressed as: data = [ ]] k ；
The analysis result comprises at least one result component data, and the function of the analysis result is expressed as: result = [ Result = [ ]] r K is not less than 1, r is not less than 1, k and r are measurement constants.
And the characteristic data items are subjected to format conversion into a data format which can be processed by the analysis model body before deep learning by utilizing each analysis model body.
And each feature component data in the feature data item is subjected to normalization processing.
The method comprises the steps of constructing an evaluation model of a land reserve analysis model by utilizing a characteristic data item and an analysis model body for constructing the land reserve analysis model, planning a land reserve analysis model with the highest adaptation degree with a target land at the target land as the target land reserve analysis model by utilizing the evaluation model based on the characteristic data item at the target land, realizing customized construction of the land reserve analysis model with the highest adaptation degree for the target land, and improving the accuracy of a land reserve analysis result of the target land.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made thereto by those skilled in the art within the spirit and scope of the present application, such modifications and equivalents should also be considered as falling within the scope of the present application.
Claims (10)
1. An intelligent planning method based on a land reserve analysis model is characterized in that: the method comprises the following steps:
s1, collecting a characteristic data item and an analysis model body for constructing a land reserve analysis model, and constructing an evaluation model of the land reserve analysis model by using the characteristic data item and the analysis model body for constructing the land reserve analysis model;
s2, planning a land reserve analysis model with the highest adaptation degree with the target land at the target land as a target land reserve analysis model by using the evaluation model based on the characteristic data item at the target land;
and S3, performing land reserve analysis on the target land by using the target land reserve analysis model.
2. An intelligent planning method based on a land reserve analysis model according to claim 1, characterized in that: the collecting of feature data items and analysis model bodies for constructing a land reserve analysis model comprises the following steps:
collecting a plurality of land areas subjected to land reserve analysis as a plurality of land samples, and extracting characteristic data items of the land samples used for land reserve analysis in each analysis time period and analysis results of the land reserve in each analysis time period;
a plurality of learning models for data analysis in the machine learning model are collected as a plurality of analysis model bodies.
3. An intelligent planning method based on a land reserve analysis model according to claim 2, characterized in that: the method for constructing the evaluation model of the land reserve analysis model by utilizing the characteristic data item and the analysis model body for constructing the land reserve analysis model comprises the following steps of:
randomly splitting the characteristic data items of each land sample in each analysis time interval and the analysis results of each analysis time interval to obtain a training set and a test set of each land sample, selecting the training set of each land sample to be applied to each analysis model body one by one to carry out deep learning to obtain a plurality of land reserve analysis models of each land sample, and calculating the accuracy of each land reserve analysis sample model of each land sample based on the test set;
taking a characteristic data item corresponding to a land reserve analysis model and an analysis model body corresponding to the land reserve analysis model as input items of a BP (back propagation) neural network, taking a correct rate corresponding to the land reserve analysis model as output items of the BP neural network, and carrying out convolution training by using the BP neural network based on the input items of the BP neural network and the output items of the BP neural network to obtain the evaluation model, wherein the model expression of the evaluation model is as follows:
P＝BP(Data,Model)；
in the formula, P is the accuracy, data is the characteristic Data item, model is the analysis Model body, and BP is the BP neural network.
4. An intelligent planning method based on a land reserve analysis model according to claim 3, characterized in that: the randomly splitting the characteristic data item of each land sample in each analysis time interval and the analysis result of each analysis time interval to obtain a training set and a testing set of each land sample at a corresponding time interval comprises the following steps:
selecting a characteristic data item of the land sample in the ith analysis period and an analysis result of the ith analysis period to form a training set, wherein a function expression of the training set is as follows: [ Data ] i ,Result i ] m ；
Selecting a characteristic data item of a land sample in a jth analysis time interval and an analysis result of the jth analysis time interval to form a training set, wherein a function expression of the training set is as follows: [ Data j ,Result j ] n ；
Wherein the Data i And Data j Respectively, the characteristic data item of the ith analysis period and the characteristic data item, result, of the jth analysis period i And Result j The analysis result of the ith analysis time interval and the analysis result of the jth analysis time interval are represented by i ≠ j, m is n =4 and is the number of the analysis time intervals in the training set, n is the number of the analysis time intervals in the testing set, and i and j are metering constants;
setting a randomly selected constraint condition as that the data dispersion difference of a training set and a test set is within a threshold range, wherein a calculation formula of the data dispersion difference of the training set and the test set is as follows:
in the formula, H is the data dispersion difference,
in order to obtain the dispersion of the data in the training set,
is the dispersion of data in the test set.
5. An intelligent planning method based on a land reserve analysis model according to claim 4, characterized in that: the construction of the target land reserve analysis model comprises the following steps:
acquiring a characteristic data item at a target soil location, combining the characteristic data item with a plurality of analysis model bodies, bringing the combined characteristic data item into an evaluation model in sequence, and outputting the accuracy of the characteristic data item and each analysis model body by the evaluation model;
selecting the analysis model body corresponding to the highest accuracy as the analysis model body of the target land, carrying out deep learning on the characteristic data item of the target land and the analysis model body of the target land to obtain a land reserve analysis model of the target land, and taking the land reserve analysis model of the target land as the target land reserve analysis model.
6. An intelligent planning method based on a land reserve analysis model according to claim 5, characterized in that: the land reserve analysis of the target land by using the target land reserve analysis model comprises the following steps:
and monitoring the characteristic data item of the target land in real time to obtain a real-time characteristic data item, and inputting the real-time characteristic data item into the target land reserve analysis model to obtain a real-time analysis result of the target land reserve.
7. An intelligent planning method based on a land reserve analysis model according to claim 6, wherein the characteristic data item comprises at least one characteristic component data, and the function of the characteristic data item is expressed as: data = [ ]] k ；
The analysis result comprises at least one result component data, and the function of the analysis result is expressed as：Result＝[result] r K is more than or equal to 1, r is more than or equal to 1, k and r are metering constants.
8. The intelligent planning method based on land reserve analysis model of claim 7, wherein the feature data items are formatted and converted into a data format that can be processed by each analysis model body before deep learning by using each analysis model body.
9. The intelligent planning method based on land reserve analysis model according to claim 8, wherein when the number of the analysis model bodies corresponding to the highest accuracy is not unique, the analysis model body corresponding to the highest operation speed is selected from the plurality of analysis model bodies corresponding to the highest accuracy as the analysis model body of the target land.
10. An intelligent planning method based on a land reserve analysis model according to claim 9, wherein each feature component data in the feature data item is normalized.
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